Modelling daily returns of New York Stock Exchange by time series with noises having stable distributions

This thesis provides some modelling procedures for the New York Stock Exchange (NYSE) daily data, using stable distributions. The use of stable distributions was motivated by the fact that traditional normality assumptions are not appropriate for the daily stock returns. Four selected series, two stock indices and two individual industrial returns, were examined. Estimates for the characteristic exponent parameter α were obtained. Following the estimation of the exponent parameter α, serial-dependence was studied by fitting autoregressive models, using two different minimization criteria. The final conclusions are the following. The four daily stock returns were stably distributed with characteristic exponent α less than two. The time series behavior of these data was suitably described by autoregressive models.

Name thesis:degree_name

Master of Science - MSc

Level thesis:degree_level

master's

Discipline thesis:degree_discipline

Statistics

Grantor dc:publisher

University of British Columbia

Year dc:date

1993

Author dc:creator

• Wang, Xiaohua

Statement dc:rights

• For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.

Language dc:language

eng

Handle dc:identifier

http://hdl.handle.net/2429/1533

OAI identifier oai:identifier

oai:circle.library.ubc.ca:2429/1533